Cold plasma jet-induced modifications in pea protein: A comparative study of gas-specific effects
Bibliographic record
Abstract
Air-classified pea protein concentrate (PPC) offers a sustainable solution for meeting the nutritional demands of a growing global population. This study investigated the effects of cold plasma (CP) jet-based non-thermal treatment, using air, nitrogen, and helium gases, on the structural, functional, and volatile profile of PPC. The diversity of reactive species generated by CP, influenced by gas type and flow rate, led to distinct modifications in treated PPC. For air and nitrogen-fed CP treatments, a noticeable reduction in the α-helix content was observed, accompanied by an increase in the random coil structures, indicating a transition process from ordered to unordered protein conformations. Functional analysis revealed that air-fed CP significantly improved protein solubility, water-holding capacity (WHC), and oil-holding capacity (OHC), while nitrogen-fed CP primarily enhanced WHC and OHC, and helium-fed CP increased OHC only at a flow rate of 4 L/min. Additionally, the CP treatment resulted in changes to the color of the pea protein, with the most pronounced bleaching effect found in samples treated by the air-fed CP. Cold plasma treatment under various conditions also yielded distinct volatile compound profiles in the treated PPC. These findings provide valuable insights for optimizing CP applications in plant protein modifications. • Type of gas supplied to the cold plasma jet greatly influenced protein changes in pea protein concentrate. • Air-fed plasma improved solubility, water and oil holding capacity, and caused major structural changes. • Nitrogen and helium plasmas mainly improved water and oil holding capacity under specific conditions. • Different cold plasma-fed gases produced distinct volatile profiles in treated pea protein concentrate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".